Quantifying gender preferences across humans lifespan

In human relations individuals' gender and age play a key role in the structures and dynamics of their social arrangements. In order to analyze the gender preferences of individuals in interaction with others at different stages of their lives we study a large mobile phone dataset. To do this we consider four fundamental gender-related caller and callee combinations of human interactions, namely male to male, male to female, female to male, and female to female, which together with age, kinship, and different levels of friendship give rise to a wide scope of human sociality. Here we analyse the relative strength of these four types of interaction using a large dataset of mobile phone communication records. Our analysis suggests strong age dependence for an ego of one gender choosing to call an individual of either gender. We observe a strong opposite sex bonding across most of their reproductive age. However, older women show a strong tendency to connect to another female that is one generation younger in a way that is suggestive of the \emph{grandmothering effect}. We also find that the relative strength among the four possible interactions depends on phone call duration. For calls of medium and long duration, opposite gender interactions are significantly more probable than same gender interactions during the reproductive years, suggesting potential emotional exchange between spouses. By measuring the fraction of calls to other generations we find that mothers tend to make calls more to their daughters than to their sons, whereas fathers make calls more to their sons than to their daughters. For younger people, most of their calls go to same generation alters, while older people call the younger people more frequently, which supports the suggestion that \emph{affection flows downward}.

[1]  Jari Saramäki,et al.  Persistence of social signatures in human communication , 2012, Proceedings of the National Academy of Sciences.

[2]  J. D. de Ruiter,et al.  Dunbar's number: group size and brain physiology in humans reexamined. , 2011, American anthropologist.

[3]  A. Barabasi,et al.  Analysis of a large-scale weighted network of one-to-one human communication , 2007, physics/0702158.

[4]  Jukka-Pekka Onnela,et al.  Geographic Constraints on Social Network Groups , 2010, PloS one.

[5]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[6]  Robin I. M. Dunbar,et al.  Social network size in humans , 2003, Human nature.

[7]  Kimmo Kaski,et al.  Communication with Family and Friends across the Life Course , 2015, PloS one.

[8]  Kimmo Kaski,et al.  Calling Dunbar's numbers , 2016, Soc. Networks.

[9]  Alessandro Vespignani,et al.  Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number , 2011, PloS one.

[10]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[11]  Vincent D. Blondel,et al.  A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.

[12]  Robin I. M. Dunbar Neocortex size as a constraint on group size in primates , 1992 .

[13]  Albert-László Barabási,et al.  Sex differences in intimate relationships , 2012, Scientific Reports.

[14]  Jari Saramäki,et al.  Temporal motifs in time-dependent networks , 2011, ArXiv.

[15]  Kimmo Kaski,et al.  Sex differences in social focus across the life cycle in humans , 2015, Royal Society Open Science.

[16]  Cliff Lampe,et al.  The Benefits of Facebook "Friends: " Social Capital and College Students' Use of Online Social Network Sites , 2007, J. Comput. Mediat. Commun..